In [1]:
import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt
In [2]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [3]:
stocks = px.data.stocks()
stocks.head()
Out[3]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [4]:
x = stocks['date']
y = stocks['GOOG']
fig, ax = plt.subplots(figsize=(20,10))
ax.plot(x,y)
ax.set_title('Goodle stock')
ax.set_xlabel('date')
ax.set_ylabel('stock value')
ax.set_xticks(range(0,stocks.shape[0],20))
plt.show()

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [5]:
x = stocks['date']
y1 = stocks['GOOG']
y2 = stocks['AAPL']
y3 = stocks['AMZN']
y4 = stocks['FB']
y5 = stocks['NFLX']
y6 = stocks['MSFT']

fig, ax = plt.subplots(figsize=(20,10))
ax.plot(x,y1,label='GOOG')
ax.plot(x,y2,label='AAPL')
ax.plot(x,y3,label='AMZN')
ax.plot(x,y4,label='FB')
ax.plot(x,y5,label='NFLX',marker='o',markerfacecolor='y',linestyle='dashdot',linewidth=2)
ax.plot(x,y6,label='MSFT')

ax.set_title('Stocks')
ax.set_xlabel('date')
ax.set_ylabel('stock value')
ax.set_xticks(range(0,stocks.shape[0],20))
ax.legend()
plt.show()

Seaborn¶

First, load the tips dataset

In [6]:
tips = sns.load_dataset('tips')
tips.head()
Out[6]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [7]:
# At what times and what types of people are more likely to giving tips?

g = sns.FacetGrid(tips, col='smoker', row='time',hue='sex')
g.map(sns.scatterplot,'total_bill','tip')
g.add_legend()
plt.show()

#People are more willing to tip at dinner than at lunch.
# And non-smokers and men will be more willing to tip.
# In general, the number of tips is proportional to the total bill.

Plotly Express¶

Question 4:¶

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [8]:
fig = px.line(
    stocks,
    x=stocks['date'],
    y=stocks.columns,
    markers=True,
)

fig.show()

The tips dataset¶

In [9]:
fig = px.scatter(
    tips,
    x='total_bill',
    y='tip',
    color='sex',
    hover_data=['sex'],
    facet_col='smoker',
    facet_row='time',

)
fig.show()

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [10]:
#load data
df = px.data.gapminder()
df.head()
Out[10]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [11]:
df = px.data.gapminder()
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()

fig = px.bar(df_2007_new,
             x='pop',
             y=df_2007_new.index,
             text_auto=True,
             color=df_2007_new.index,
             orientation='h'
             
            )
fig.update_traces(textposition='outside')
fig.update_yaxes(categoryorder='total ascending')

fig.show()